Choosing robust solutions in discrete optimization problems with fuzzy costs

In this paper a wide class of discrete optimization problems, which can be formulated as a 0–1 linear programming problem is discussed. It is assumed that the objective function costs are not precisely known. This uncertainty is modeled by specifying a finite set of fuzzy scenarios. Under every fuzzy scenario the costs are given as fuzzy intervals. Possibility theory is then applied to chose a solution in such a problem and mixed integer linear programming models are proposed to compute this solution.